LAGAM: A Length-Adaptive Genetic Algorithm With Markov Blanket for High-Dimensional Feature Selection in Classification

Junhai Zhou, Quanwang Wu, Mengchu Zhou, Junhao Wen, Yusuf Al-Turki, Abdullah Abusorrah

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Feature selection (FS) is an essential technique widely applied in data mining. Recent studies have shown that evolutionary computing (EC) is very promising for FS due to its powerful search capability. However, most existing EC-based FS methods use a length-fixed encoding to represent feature subsets. This inflexible encoding turns ineffective when high-dimension data are handled, because it results in a huge search space, as well as a large amount of training time and memory overhead. In this article, we propose a length-adaptive genetic algorithm with Markov blanket (LAGAM), which adopts a length-variable individual encoding and enables individuals to evolve in their own search space. In LAGAM, features are rearranged decreasingly based on their relevance, and an adaptive length changing operator is introduced, which extends or shortens an individual to guide it to explore in a better search space. Local search based on Markov blanket (MB) is embedded to further improve individuals. Experiments are conducted on 12 high-dimensional datasets and results reveal that LAGAM performs better than existing methods. Specifically, it achieves a higher classification accuracy by using fewer features.

Original languageEnglish (US)
Pages (from-to)6858-6869
Number of pages12
JournalIEEE Transactions on Cybernetics
Issue number11
StatePublished - Nov 1 2023

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Science Applications


  • Classification
  • Markov blanket (MB)
  • feature selection (FS)
  • genetic algorithms (GAs)
  • high-dimensional data
  • length-adaptive
  • machine learning


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